code: def decode(
      self,
      z: Tensor,
      *,
      resize: bool = True,
      apply_codebook: bool = True,
      apply_tanh: Optional[bool] = None,
  ) -> Tensor:
      if apply_codebook:
          z = self.codebook(z).z_q
      net = self.from_embedding(z)
      kw = dict(resize=resize, apply_tanh=apply_tanh)
      net = self.generator.decode({INPUT_KEY: net}, **kw)  # type: ignore
      return net
  
imports: import torch
  import torch.nn
  from torch import Tensor
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .common import IAutoEncoder
  from .common import AutoEncoderInit
  from .common import AutoEncoderLPIPSWithDiscriminator
  from ..general import VQCodebook
  from ...schemas import CustomTrainStepLoss
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
  from ....losses.vae import VQVAELoss
label_desc: Use image node_id 3 for calling the AutoEncoderVQ obj's underlying member method code with example usage: obj.decode(z) and returns: net